REVIEW 5 major objections 6 minor 20 references
VEIGAR: View-consistent Explicit Inpainting and Geometry Alignment for 3D object Removal
T0 review · 5 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read VEIGAR achieves 3D object removal at triple speed, without an initial reconstruction phase, while keeping views consistent.
desk verdict A faster, plausible 3D object-removal pipeline whose SOTA claim is undercut by thin evaluation and an unvalidated assumption about mask-interior depth. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing mechanism is a two-stage geometry-alignment chain. In the first stage, a deep stereo depth estimate for the anchor view is refined by a small network $F$ trained only near the mask boundary through $\mathcal{L}_{\mathrm{comp}} = \sum_{p \in \partial M} (F(d(p)) - D(p))^2$, and an implicit intrinsic matrix $\tilde{K}$ is fitted by matching predicted depth against sparse SfM depth through $\mathcal{L}_{\mathrm{intr}} = \sum_{p \in M} (d(p) - D_{\mathrm{sfm}}(p))^2$. These two pieces let the pipeline lift the anchor's inpainted pixels into 3D and project them into target views before any scene representation exists. In the second stage, the scale-invariant depth loss $\mathcal{L}_{\mathrm{SI}} = \frac{1}{n}\sum_i (\log D_i - \log D_i^*)^2 - \frac{1}{n^2}\left(\sum_i (\log D_i - \log D_i^*)\right)^2$ supervises 3D Gaussian Splatting on relative depth structure, so a uniform scaling of predicted depth does not change the loss and the usual scale-and-shift alignment step disappears.
What would settle it
Measure the corrected anchor-view depth inside the removed mask and compare it to a trustworthy depth map of the true background, available in a synthetic scene or from dense multi-view stereo computed without the object. If the interior depth is wrong while boundary pixels fit, the boundary-only supervision in Eq. (3) is the point of failure; a simpler ablation that supervises Eq. (3) on all mask pixels and checks whether LoFTR or PSNR changes materially would settle it.
Extended reading notes
Core claim
The paper's central claim is that a 3D object-removal pipeline can achieve strong cross-view consistency without any initial reconstruction phase or latent-space alignment. VEIGAR inpaints one anchor view, refines its depth with a lightweight network supervised only at mask-boundary pixels, fits an implicit intrinsic matrix $\tilde{K}$ by reprojecting sparse SfM points, and projects the inpainted content into every other view. A pretrained inpainter completes those projected views, and 3D Gaussian Splatting is trained under a scale-invariant depth loss that compares log-depth differences against their mean, so global scale cancels out. On the ten-scene SPIn-NeRF benchmark the paper reports the best masked-region scores among tested baselines on PSNR (16.385, pixel fidelity), LPIPS (0.524, perceptual similarity), and LoFTR (222.428, feature-match consistency across views), with a training time of 0.42 hours versus 1.20 hours for GScream.
Load-bearing premise
The method assumes that a depth-correction network trained only around the mask border computes correct depth in the middle of the removed region, and that the camera matrix fitted from sparse points projects those depths accurately; neither is measured in the paper.
Editorial extensions
If this is right
- If the central claim holds, object removal can reach comparable or better quality without the initial reconstruction phase, cutting training time on this benchmark to roughly 0.42 hours, about three times faster than GScream's 1.20 hours.
- The scale-invariant depth loss is presented as a general plug-in: adding it to MVInpainter raises its LoFTR score from 73.144 to 87.262 and improves LPIPS, suggesting other pipelines can adopt it without metric-depth calibration.
- Because VEIGAR aligns inpainted content explicitly in pixel space, the paper argues that no latent-space diffusion alignment is needed, and it reports higher perceptual fidelity than baselines that train with LPIPS-based losses even though VEIGAR does not use one.
- The higher LoFTR score (222.428 versus 215.166 for GScream) supports the paper's claim that the projected inpaintings remain structurally coherent across viewpoints.
Reading between the lines
- The paper does not report depth accuracy inside the removed mask, so the unstated crux is whether the boundary-trained network $F$ also gets interior depth right; that is the first thing a reader should probe.
- Because Eq. (3) supervises only mask-boundary pixels, a natural extension is to add sparse interior depth constraints from multi-view stereo and measure whether LoFTR and PSNR improve; the current design leaves that headroom implicit.
- The scale-invariant loss should transfer beyond object removal, for example to few-shot reconstruction or NeRF editing, since it only needs rendered and predicted depth pairs; the MVInpainter ablation already points in that direction.
- The reported 3x speedup is measured against the fastest baseline on one ten-scene benchmark; whether it persists across larger, more varied scenes and with multiple anchor views is an open question the paper's single-anchor design invites.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces VEIGAR, a 3D Gaussian Splatting framework for object removal. In the first stage, an anchor view is inpainted with a pretrained model; deep stereo depth and an implicitly estimated intrinsic matrix K~ are used to project the inpainted anchor into all other views, after which a pretrained inpainting network completes each projected view. In the second stage, 3DGS is optimized with photometric loss plus a scale-invariant depth supervision loss (Eqs. 5-6). Experiments on the ten-scene SPIn-NeRF benchmark report PSNR/LPIPS/LoFTR (Table 1) and an ablation of the depth loss (Table 2), claiming state-of-the-art quality and consistency with roughly 3x faster training.
Significance. The core idea is attractive: explicit pixel-space projection avoids the expensive initial reconstruction used by methods like GScream and In-and-Out, and the scale-invariant depth loss removes scale-and-shift alignment. If the claims hold across more benchmarks, the efficiency gain alone is practically valuable, and the depth-loss ablation on MVInpainter suggests a transferable mechanism. The paper provides a clear pipeline description, standard benchmark comparison, and training-time measurements. However, the evidence for the headline claim is currently thin: all conclusions rest on ten scenes, one aggregated table without variance, and the projection stage that the whole cross-view consistency argument depends on is never quantitatively validated.
major comments (5)
- [Section 5.3 / Table 1] The claim that VEIGAR 'consistently outperforms' all baselines across all evaluation metrics is not statistically supported. Results are averages over only ten scenes with no per-scene breakdown, no standard deviations, and no significance tests; the PSNR advantage over the strongest baseline (MV-Inpainter) is 16.385 vs. 16.320, a difference of 0.065 dB. Please provide per-scene results and error bars or significance tests before asserting a new state of the art.
- [Section 4.1 / Eq. (3)] The depth correction network F is supervised only on mask-boundary pixels ∂M, yet F's output is used to lift the entire mask interior for projection to other views. No depth error or reprojection error is reported for pixels inside the mask. This is the load-bearing step for cross-view consistency: if F is inaccurate inside the mask, the projected inpaintings are misaligned and the LoFTR gain in Table 1 may reflect the 3DGS optimization rather than correct projection. Please add a direct evaluation of F inside the mask, for example comparing F(d) with DSD depth on non-masked pixels of held-out views or measuring the reprojection error of inpainted pixels.
- [Section 4.1 / Eq. (4)] The implicit intrinsic matrix K~ is fitted from sparse SfM points in the anchor view, but the paper does not report the residual of this fit or analyze whether the estimated K~ transfers correctly to other views. If the fit absorbs a per-scene scale or shift, projections into target views will be systematically biased. Please report the K~ residual and an alignment metric for projected views (e.g., mean pixel displacement or reprojection success rate) across the ten scenes.
- [Section 5.4 / Table 2] The ablation shows that the scale-invariant depth loss lowers PSNR for the full method (16.444 without vs. 16.385 with) while improving LPIPS and LoFTR. The text says 'PSNR shows slight variations', but in the reported aggregate the drop is consistent. Since the paper's 'reconstruction quality' claim uses PSNR, the authors should analyze this trade-off per scene and either justify the PSNR drop or temper the claim that LSI improves all aspects.
- [Section 5.1] The description of the LoFTR consistency metric is incomplete: it does not specify which image pairs are matched, whether the metric is computed on rendered outputs or on training views, or how a single scalar is obtained from the match counts. Without this protocol, the central cross-view consistency result is hard to interpret and replicate.
minor comments (6)
- [Abstract / Section 5.3] The phrase 'new state-of-the-art benchmark' is overstated given the single-dataset evaluation and missing statistical analysis; consider 'competitive on SPIn-NeRF' or explicitly qualify the claim.
- [Eq. (5)] The variables D_i and D*_i are not defined in the text; state clearly which one is the rendered depth and which one is the target monocular depth.
- [Section 5.1] The baseline 'LaMask' is cited as Mirzaei et al. [2023], which is the same reference as SPIn-NeRF; please clarify what LaMask is and provide the correct source.
- [Table 1] The training-time comparison should specify that MVInpainter is excluded because it has no reconstruction phase, and the 3x figure should be stated as approximate (0.42 hrs vs. 1.20 hrs is a factor of 2.86).
- [Section 4 / Figure 2] The contributions of the depth-correction network F and the intrinsic estimation K~ are not isolated by any ablation; only the scale-invariant loss is ablated, so the relative importance of the projection components remains unknown.
- [Section 5.2] The sentence 'We considered our method with representative one using scaled-monodepth' is grammatically unclear and should be rewritten.
Circularity Check
No significant circularity: VEIGAR's intermediate fits are externally supervised and benchmarked, so the derivation chain is self-contained.
full rationale
I found no load-bearing circular step. The paper's intermediate quantities (the depth-correction network F in Eq. (3) and the implicit intrinsics K-tilde in Eq. (4)) are fitted to SfM and DSD boundary data, but they are not the target of the paper; the final 3DGS reconstruction is evaluated on held-out test views with PSNR, LPIPS, and LoFTR against ground truth. The scale-invariant loss of Eq. (5) is explicitly adopted from Wang et al. (2021a), not derived from the method's own output, and the paper does not claim to have invented that loss formula. The conclusion's stated limitations (sensitivity to the inpainting model, single-anchor insufficiency) are correctness concerns, not circularity. There is no self-citation chain or uniqueness argument; the method is externally benchmarked against SPIn-NeRF, GScream, MVInpainter, In-and-Out, and LaMask. The reader's concern about unvalidated interior depth is a legitimate robustness limitation, but it does not make any derivation equivalent to its inputs, so the circularity score is 0.
Assumptions & free parameters
free parameters (2)
- λdepth, λcolor, λssim (loss weights in Eq. 6) =
not reported
- Implicit intrinsic matrix K~ =
estimated per scene from sparse SfM points; values not reported
assumptions (4)
- domain assumption Deep stereo depth estimates are view-consistent and accurate in observed regions.
- ad hoc to paper The boundary-trained correction network generalizes inside the inpainted mask.
- domain assumption Sparse SfM points are sufficient to determine the implicit intrinsic matrix.
- domain assumption Monocular depth estimates are reliable enough for scale-invariant supervision during 3DGS training.
Cite this review
Pith. "Pith review of VEIGAR: View-consistent Explicit Inpainting and Geometry Alignment for 3D object Removal." pith.science (2026). https://pith.science/paper/TSD6WGLD
@misc{pith2026250615821,
author = {Pith},
title = {Pith review of: VEIGAR: View-consistent Explicit Inpainting and Geometry Alignment for 3D object Removal},
year = {2026},
howpublished = {\url{https://pith.science/paper/TSD6WGLD}},
note = {Machine review of arXiv:2506.15821}
}
read the original abstract
Recent advances in Novel View Synthesis (NVS) and 3D generation have significantly improved editing tasks, with a primary emphasis on maintaining cross-view consistency throughout the generative process. Contemporary methods typically address this challenge using a dual-strategy framework: performing consistent 2D inpainting across all views guided by embedded priors either explicitly in pixel space or implicitly in latent space; and conducting 3D reconstruction with additional consistency guidance. Previous strategies, in particular, often require an initial 3D reconstruction phase to establish geometric structure, introducing considerable computational overhead. Even with the added cost, the resulting reconstruction quality often remains suboptimal. In this paper, we present VEIGAR, a computationally efficient framework that outperforms existing methods without relying on an initial reconstruction phase. VEIGAR leverages a lightweight foundation model to reliably align priors explicitly in the pixel space. In addition, we introduce a novel supervision strategy based on scale-invariant depth loss, which removes the need for traditional scale-and-shift operations in monocular depth regularization. Through extensive experimentation, VEIGAR establishes a new state-of-the-art benchmark in reconstruction quality and cross-view consistency, while achieving a threefold reduction in training time compared to the fastest existing method, highlighting its superior balance of efficiency and effectiveness.
Figures
Figures from the paper (3 more)
Reference graph
Works this paper leans on
-
[2]
URL https://doi.org/10.1109/CVPR52733.2024.00764
doi: 10.1109/CVPR52733.2024.00764. URL https://doi.org/10.1109/CVPR52733.2024.00764. S. F. Bhat, R. Birkl, D. Wofk, P. Wonka, and M. Müller. Zoedepth: Zero-shot transfer by combining relative and metric depth,
arXiv 2024
-
[4]
URL https://arxiv.org/abs/ 2408.08000. L. Cerkezi, A. Davtyan, S. Sameni, and P. Favaro. Multi-view unsupervised image generation with cross attention guidance. arXiv preprint arXiv:2312.04337,
-
[5]
URL https://arxiv.org/ abs/2312.04337. Y . Chen, Z. Chen, C. Zhang, F. Wang, X. Yang, Y . Wang, Z. Cai, L. Yang, H. Liu, and G. Lin. Gaussianeditor: Swift and controllable 3d editing with gaussian splatting,
- [6]
-
[7]
URL https://proceedings.neurips.cc/paper_files/paper/2024/file/ 510d0935b543a29d686f93fa52d1c288-Paper-Conference.pdf . S.-Y . Huang, Z.-T. Chou, and Y .-C. F. Wang. 3d gaussian inpainting with depth-guided cross-view consistency. arXiv preprint arXiv:2502.11801,
work page Pith review arXiv 2024
-
[9]
doi: 10.1109/ICCV .2017.17. B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42(4), July
doi:10.1109/iccv 2017
-
[11]
URL https://arxiv.org/abs/2404.11613. Y . Lu, J. Ma, and Y . Yin. View-consistent object removal in radiance fields. InProceedings of the 32nd ACM International Conference on Multimedia , pages 3597–3606,
-
[13]
URL https://arxiv.org/abs/2209.14988. S. Qian et al. Make-a-video: Text-to-video generation without text-video data. arXiv preprint arXiv:2209.14792,
Show all 20 references
-
[17]
Xu and S
Y . Xu and S. Song. Denseposediffusion: Multiview consistent human reconstruction with 2d diffusion priors. arXiv preprint arXiv:2304.00501,
-
[18]
G. Yang, X. Zhang, M. Tschannen, K. Hou, L. Beyer, B. Ommer, and M. Lucic. Diffusion models for video generation. arXiv preprint arXiv:2204.03458,
-
[19]
L. Yang, B. Kang, Z. Huang, X. Xu, J. Feng, and H. Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024a. URL https://arxiv.org/abs/2306.05425. L. Yang, B. Ka...
-
[20]
S.-Y . Yu, K. Lin, S. Belongie, and T. Darrell. Dream3d: Zero-shot text-to-3d synthesis using 3d shape prior and text-to-image diffusion models. arXiv preprint arXiv:2302.00434,
-
[21]
Zhang, Y
W. Zhang, Y . Hu, Y . Liu, H. Bao, and G. Zhang. 3d generation via gaussian splatting and diffusion priors. In arXiv preprint arXiv:2311.18551, 2023a. Y . Zhang, J. Fu, K. Lin, T. Darrell, and S. Belongie. Diffusion models for view synthesis with learnable camera embeddings. a...
-
[2017]
Kendall, H
A. Kendall, H. Martirosyan, S. Dasgupta, P. Henry, R. Kennedy, A. Bachrach, and A. Bry. End- to-end learning of geometry and context for deep stereo regression. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 66–75,
2017
-
[2018]
S. Liu, K. Lin, Z. Yu, H. Zhang, T. Darrell, and S. Belongie. Zero-1-to-3: Zero-shot one image to 3d object. arXiv preprint arXiv:2303.11328,
-
[2021]
L. Wang, Y . Wang, L. Wang, Y . Zhan, Y . Wang, and H. Lu. Can scale-consistent monocular depth be learned in a self-supervised scale-invariant manner? In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 12707–12716, 2021a. doi: 10.1109/ICCV48922.2021.01...
2021
-
[2022]
Metzer, L
G. Metzer, L. Yariv, M. Atzmon, T. Dekel, and Y . Lipman. Latent-nerf for shape-guided generation of 3d shapes and textures. arXiv preprint arXiv:2211.07600,
-
[2023]
URL https://arxiv.org/abs/2302.12288. C. Cao, C. Yu, F. Wang, X. Xue, and Y . Fu. Mvinpainter: Learning multi-view consistent inpainting to bridge 2d and 3d editing. In Proceedings of NeurIPS,
-
[2024]
URL https://doi.org/10.1007/978-3-031-73021-4_1
doi: 10.1007/978-3-031-73021-4_1. URL https://doi.org/10.1007/978-3-031-73021-4_1 . O. Avrahami, A. Kirillov, M. Shahar, and D. Cohen-Or. Blended latent diffusion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ,
-
[2025]
URL https://arxiv.org/abs/2502.13335. J. L. Schonberger and J.-M. Frahm. Structure-from-motion revisited. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June
Reviewed August 7, 2026 · model on record in the stance chip above.
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